Wednesday, September 20, 2017

Watch all the sessions from Red Hat Drools Day LIVE from your desktop or mobile, Sept 26th

We will be streaming all the sessions of the Drools Day in NYC, on Sep 26th, live!

Use the following link to watch:

http://red.ht/2wuOgi1

Or watch it here:




Share/Bookmark

Wednesday, September 13, 2017

Drools, jBPM and Optaplanner Day: September 26 / 28, 2017 (NY / Washington)

Red Hat is organizing a Drools, jBPM and Optaplanner Day in New York and Washington DC later this year to show how business experts and citizen developers can use business processes, decisions and other models to develop modern business applications.
This free full day event will focus on some key aspects and several of the community experts will be there to showcase some of the more recent enhancements, for example:
  • Using the DMN standard (Decision Model and Notation) to define and execute decisions
  • Moving from traditional business processes to more flexible and dynamic case management
  • The rise of cloud for modeling, execution and monitoring
IT executives, architects, software developers, and business analysts who want to learn about the latest open source, low-code application development technologies.

Detailed agenda and list of speakers can be found on each of the event pages.

Places are limited, so make sure to register asap !

Share/Bookmark

Wednesday, September 06, 2017

Is Optimization AI or OR?

With the renewed interest in AI the same conversations are starting to come up again, about what is or isn't AI.  My recent discussion was on whether optimisation products, such as OptaPlanner, are considered AI as some considered it more Operations Research (OR). For some background, OptaPlanner started out as a Tabu Solver implementation, but has since added other techniques like Simulated Annealing.

Although I'd like to add that no single technique is AI, they are all tools and techniques that are quite typically used together in a blended, hybrid or integrated AI solution. So it's the right tool or tools for the job.

The answer is that Optimisation is both an AI and an OR problem. It is a technique used and researched by both groups,  the two different disciplines tend to take different approaches to the problem, having differing use cases and have historically used different techniques, with a lot of cross pollination from both sides.

I'll start with a consumer oriented answer to the question. StaffJoy has a nice blog article on the overlap of OR and AI, and I'll quote from that below:
https://blog.staffjoy.com/how-operations-research-and-artificial-intelligence-overlap-b128a3efee2e
"Startups are using OR techniques in products like OnFleet, Instacart, and Lyft Line. However, when similar techniques are being exposed externally as services, they are often described as AI — e.g. x.ai, Atomwise, and Sentient. Very few companies describe algorithms that they sell as optimization (with the exception of SigOpt) because the end goal of customers is automating decisions. With StaffJoy, we have found that customers better understand our product when we describe it as an “artificial intelligence” tool rather than an “optimization” or “operations” tool. We think that this is because customers care more about what a product achieves, rather than the means it uses to achieve it."

In short consumers do not see the difference between OR and AI, when applied to real world problems and it is commonly marketed as AI.

I'll go a little more technical now, to further demonstrate it's more than just marketing - as that side is only touched on in the above blog post.

While the two groups (OR and AI) may have once been distinct, it's been well established that the OR and AI groups overlap in this space and have collaborated for years. Glover (1986) states them as "the recent remarriage of two disciplines that were once united, having issued from a common origin, but which became separated" - see final paper link at end.

A cursory google with terms "operations research" and "artificial intelligence" will more than prove this. Some techniques, like Linear Programming, are strongly on the OR side, others like Local Search (which OptaPlanner falls under) are shared. Optimisation, and local search (along with other techniques), is a core fundamental taught in every AI course without fail, and will be covered in every general AI book, used in schools - such as "AI:  A Modern Approach"- see chapter 4, page 120
http://aima.cs.berkeley.edu/contents.html

The book "Artificial Intelligence Methods and Applications" also makes it clear the two (OR and AI) are linked:
"Local search, or local optimisation, is one of the primitive forms of continuous optimisation in a discrete problem space. It was one of the early techniques proposed during the mid sixties to cope with the overwhelming computational intractability of NP-hard combinatorial optimisation problems. Unlike continuous optimisation techniques, local search has often been used in AI research and has established a strong link between AI and the operational research area."
https://books.google.co.uk/books?id=0a_j0R0qh1EC&pg=PA67&lpg=PA67&dq=%22local+search%22+operations+research+artificial+intelligence&source=bl&ots=h2jGquBm4d&sig=2V7CKRIs3ZL-NKzqL3Dnkx33_NI&hl=en&sa=X&redir_esc=y#v=onepage&q=%22local%20search%22%20operations%20research%20artificial%20intelligence&f=false

Lastly I'll quote directly from the original Tabu Solver paper "These developments may be usefully viewed as a synthesis of the perspectives of operations research and artificial intelligence... ... The foundation for this prediction derives, perhaps surprisingly, from the recent remarriage of two disciplines that were once united, having issued from a common origin, but which became separated and maintained only loose ties for several decades: operations research and artificial intelligence. This renewed reunion is highlighting limitations in the frameworks of each (as commonly applied, in contrast to advocated), and promises fertile elaborations to the strategies each has believed fruitful or approaching combinatorial complexity." Glover (1986). Note the paper is from "The Center of Applied Artificial Intelligence". 
http://leeds-faculty.colorado.edu/glover/TS%20-%20Future%20Paths%20for%20Integer%20Programming.pdf

So I hope that clears that up - AI is a very broad church :)



Share/Bookmark